History Server & API

1. History Server & API Christopher Larrieu Jefferson Laboratory

2. Goals • Simple interface to archived data • Efficient access to distributed data sources • Useful data manipulation capabilities • Portable integration with existing EPICS tools

3. CADataStore: abstract interface to read CA data from file, db, etc. • archive • Provides channel listing, creates data range objects • range • Describes data for one channel on time range • channel_state • Complete information for one channel associated with one point in time. • range_iterator • Access sequential channel states by updating channel_state

4. Server Specialization • Server accesses CA archive via CADataStore object • Different derived classes for different archives • LANL_DataStore wraps around Kay’s archive API • JLAB_DataStore wraps around JLAB archive API • RDB_DataStore ? • *_DataStore • Derived classes compiled as shared object code, loaded at run-time. • Narrow interface eases specialization, but captures essential functionality.

5. Server Schematic History Data Client History Server LANL_DataStore Channel Archiver API

6. Server Components • Data access plug-in (derived from CADataStore) • Exported network interface • Archive description (name, acquisition mode, etc.) • Data manipulation (transform library) • Data production (data stream, iterator) • Network communication component

7. Client Components • Consumer code (e.g. data mining, visualization) • Library API • Directory Services • Data manipulation (transform library) • Data consumption (stream from server) • Network communication component

8. DataStoreDirectory • Provides hierarchical structure to signal names • Enumerates registered history servers • Name • Location • Data quality • Maps signal names to history servers

9. DataSequence • Describes a stream of sequenced data • Every element contains • Indexing attribute (scalar, numeric) (e.g. time) • Primary attribute (e.g. value) • Ancillary attributes (e.g. status, severity, units) • Attribute dependencies (e.g. alarm depends upon value) • Elements are iterated over via the indexing attribute, and return primary attribute as default value.

10. DataTransform • Reads input sequence, generates output sequence. • Generic transforms operate upon primary attribute. • Specialized transforms operate on known attributes.

11. Example 1: Binning Transform • Parameters: bin width • Input: (index,value) pairs (e.g. time, value) • All other attributes discarded • Output: (count, mean, variance, min, max) tuples, indexed by bin number, with mean as primary attribute.

12. Example 2: Discrete Fourier Transform • Input (index, value) pairs (e.g. time, value) • All other attributes discarded • Output (frequency, amplitude) pairs, index by frequency, with amplitude as primary attribute.

13. Standard Transforms • Binning • Interpolating • Decimating • DFT, IDFT • Digital Filter • Averaging • Rasterizing • Attribute Mapping • Attribute Filtering

14. Network Communication • CORBA • Transparent binary compatibility • Easy communication between distributed objects • Integrate with other CORBA-enabled tools • MICO • open source implementation • branded CORBA 2.3 compliant by OMG • Portable: Unix (hp-ux, linux, solaris, aix, etc.), Win32 • C++, Java bindings

15. Uses in EPICS • Portable CA Server interface can supply history data to ca clients (e.g. medm) . • Adaptor library supporting Kay’s API: current applications become network capable. • New applications: java tools, etc.

16. Intended Use at JLAB • StripTool history plug-in • Replacement for current XARR interface • Operator / user scripting. • DARTH (vaporous data-mining tool)